# Configure a space
<a name="studio-updated-jl-user-guide-configure-space"></a>

After you create a JupyterLab space, you can configure it to do the following:
+ Change the instance type.
+ Change the storage volume.
+ (Admin set up required) Use a custom image.
+ (Admin set up required) Use a lifecycle configuration.
+ (Admin set up required) Attach a custom Amazon EFS.

**Important**  
You must stop the JupyterLab space every time you configure it. Use the following procedure to configure the space.

**To configure a space**

1. Within Studio, navigate to the JupyterLab application page.

1. Choose the name of the space.

1. (Optional) For **Image**, specify an image that your administrator provided to customize your environment.
**Important**  
Custom IAM policies that allow Studio users to create spaces must also grant permissions to list images (`sagemaker: ListImage`) to view custom images. To add the permission, see [ Add or remove identity permissions](https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies_manage-attach-detach.html) in the *AWS Identity and Access Management* User Guide.   
[AWS managed policies for Amazon SageMaker AI](security-iam-awsmanpol.md) that give permissions to create SageMaker AI resources already include permissions to list images while creating those resources.

1. (Optional) For **Space Settings**, specify the following:
   + **Storage (GB)** – Up to 100 GB or the amount that your administrator configured for the space.
   + **Lifecycle Configuration** – A lifecycle configuration that your administrator provides.
   + **Attach custom EFS filesystem** – An Amazon EFS to which your administrator provides access.

1. Choose **Run space**.

When you open the JupyterLab application, your space has the updated configuration.